Prediction of wafer state after plasma processing using real-time tool data

Empirical models based on real-time equipment signals are used to predict the outcome (e.g., etch rates and uniformity) of each wafer during and after plasma processing. Three regression and one neural network modeling methods were investigated. The models are verified on data collected several weeks after the initial experiment, demonstrating that the models built with real-time data survive small changes in the machine due to normal operation and maintenance. The predictive capability can be used to assess the quality of the wafers after processing, thereby ensuring that only wafers worth processing continue down the fabrication line. Future applications include real-time evaluation of wafer features and economical run-to-run control. >

[1]  Richard P. Lippmann,et al.  An introduction to computing with neural nets , 1987 .

[2]  Bernard Widrow,et al.  30 years of adaptive neural networks: perceptron, Madaline, and backpropagation , 1990, Proc. IEEE.

[3]  James P. McVittie,et al.  SPEEDIE: a profile simulator for etching and deposition , 1991, Other Conferences.

[4]  C. Spanos,et al.  Statistical experimental design in plasma etch modeling , 1991 .

[5]  Costas J. Spanos,et al.  Real-time statistical process control using tool data (semiconductor manufacturing) , 1992 .

[6]  Costas J. Spanos,et al.  Semiconductor yield improvement: results and best practices , 1995 .

[7]  C. J. Spanos,et al.  Equipment Analysis And Wafer Parameter Prediction Using Real-Time Tool Data , 1994, International Symposium on Semiconductor Manufacturing, Extended Abstracts of ISSM.

[8]  Costas J. Spanos,et al.  A Control & Diagnosis Scheme for Semiconductor Manufacturing , 1993, 1993 American Control Conference.

[9]  Gary S. May,et al.  Advantages of plasma etch modeling using neural networks over statistical techniques , 1993 .

[10]  George E. P. Box,et al.  Empirical Model‐Building and Response Surfaces , 1988 .

[11]  R. Gottscho,et al.  Design of High-Density Plasma Sources for Materials Processing , 1994 .

[12]  G. G. Stokes.  "J." , 1890, The New Yale Book of Quotations.

[13]  M. Lieberman,et al.  Analytic model of the ion angular distribution in a collisional sheath , 1993 .

[14]  Derek J. Pike,et al.  Empirical Model‐building and Response Surfaces. , 1988 .

[15]  Costas J. Spanos,et al.  RTSPC: a software utility for real-time SPC and tool data analysis , 1995 .

[16]  Edward A. Rietman,et al.  Use of neural networks in modeling semiconductor manufacturing processes: an example for plasma etch modeling , 1993 .

[17]  Terry R. Turner,et al.  Etch process characterization using neural network methodology: a case study , 1992, Other Conferences.

[18]  J. Friedman,et al.  A Statistical View of Some Chemometrics Regression Tools , 1993 .

[19]  Gary S. May,et al.  An optimal neural network process model for plasma etching , 1994 .

[20]  Alice M. Agogino,et al.  Use of influence diagrams and neural networks in modeling semiconductor manufacturing processes , 1991 .